In [ ]:
%%HTML
<script src="require.js"></script>
In [ ]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import seaborn as sns
In [ ]:
#We read data
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00373/drug_consumption.data')
#Set max columns to 31
pd.set_option('display.max_columns', 31)
cols = ["ID", "Age", "Gender", "Education", "Ethnicity", "Country", "Nscore", "Escore", "Oscore", "Ascore", "Cscore", "Impulsive", "SS", "Alcohol", "Amphet", "Amyl", "Benzos", "Caff", "Cannabis", "Choc", "Coke", "Crack", "Ecstasy", "Heroin", "Ketamine", "Legalh", "LSD", "Meth", "Mushrooms", "Nicotine", "Semer", "VSA"]
df.columns = cols
df.set_index("ID", inplace=True)
df
Out[ ]:
Age Gender Education Ethnicity Country Nscore Escore Oscore Ascore Cscore Impulsive SS Alcohol Amphet Amyl Benzos Caff Cannabis Choc Coke Crack Ecstasy Heroin Ketamine Legalh LSD Meth Mushrooms Nicotine Semer VSA
ID
2 -0.07854 -0.48246 1.98437 0.96082 -0.31685 -0.67825 1.93886 1.43533 0.76096 -0.14277 -0.71126 -0.21575 CL5 CL2 CL2 CL0 CL6 CL4 CL6 CL3 CL0 CL4 CL0 CL2 CL0 CL2 CL3 CL0 CL4 CL0 CL0
3 0.49788 -0.48246 -0.05921 0.96082 -0.31685 -0.46725 0.80523 -0.84732 -1.62090 -1.01450 -1.37983 0.40148 CL6 CL0 CL0 CL0 CL6 CL3 CL4 CL0 CL0 CL0 CL0 CL0 CL0 CL0 CL0 CL1 CL0 CL0 CL0
4 -0.95197 0.48246 1.16365 0.96082 -0.31685 -0.14882 -0.80615 -0.01928 0.59042 0.58489 -1.37983 -1.18084 CL4 CL0 CL0 CL3 CL5 CL2 CL4 CL2 CL0 CL0 CL0 CL2 CL0 CL0 CL0 CL0 CL2 CL0 CL0
5 0.49788 0.48246 1.98437 0.96082 -0.31685 0.73545 -1.63340 -0.45174 -0.30172 1.30612 -0.21712 -0.21575 CL4 CL1 CL1 CL0 CL6 CL3 CL6 CL0 CL0 CL1 CL0 CL0 CL1 CL0 CL0 CL2 CL2 CL0 CL0
6 2.59171 0.48246 -1.22751 0.24923 -0.31685 -0.67825 -0.30033 -1.55521 2.03972 1.63088 -1.37983 -1.54858 CL2 CL0 CL0 CL0 CL6 CL0 CL4 CL0 CL0 CL0 CL0 CL0 CL0 CL0 CL0 CL0 CL6 CL0 CL0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1884 -0.95197 0.48246 -0.61113 -0.57009 -0.31685 -1.19430 1.74091 1.88511 0.76096 -1.13788 0.88113 1.92173 CL5 CL0 CL0 CL0 CL4 CL5 CL4 CL0 CL0 CL0 CL0 CL0 CL3 CL3 CL0 CL0 CL0 CL0 CL5
1885 -0.95197 -0.48246 -0.61113 -0.57009 -0.31685 -0.24649 1.74091 0.58331 0.76096 -1.51840 0.88113 0.76540 CL5 CL0 CL0 CL0 CL5 CL3 CL4 CL0 CL0 CL2 CL0 CL0 CL3 CL5 CL4 CL4 CL5 CL0 CL0
1886 -0.07854 0.48246 0.45468 -0.57009 -0.31685 1.13281 -1.37639 -1.27553 -1.77200 -1.38502 0.52975 -0.52593 CL4 CL6 CL5 CL5 CL6 CL6 CL6 CL4 CL0 CL4 CL0 CL2 CL0 CL2 CL0 CL2 CL6 CL0 CL0
1887 -0.95197 0.48246 -0.61113 -0.57009 -0.31685 0.91093 -1.92173 0.29338 -1.62090 -2.57309 1.29221 1.22470 CL5 CL0 CL0 CL0 CL6 CL6 CL5 CL0 CL0 CL3 CL0 CL0 CL3 CL3 CL0 CL3 CL4 CL0 CL0
1888 -0.95197 -0.48246 -0.61113 0.21128 -0.31685 -0.46725 2.12700 1.65653 1.11406 0.41594 0.88113 1.22470 CL4 CL3 CL0 CL3 CL6 CL3 CL6 CL3 CL0 CL3 CL0 CL0 CL3 CL3 CL0 CL3 CL6 CL0 CL2

1884 rows × 31 columns

In [ ]:
#We check the data types
df.dtypes
Out[ ]:
Age          float64
Gender       float64
Education    float64
Ethnicity    float64
Country      float64
Nscore       float64
Escore       float64
Oscore       float64
Ascore       float64
Cscore       float64
Impulsive    float64
SS           float64
Alcohol       object
Amphet        object
Amyl          object
Benzos        object
Caff          object
Cannabis      object
Choc          object
Coke          object
Crack         object
Ecstasy       object
Heroin        object
Ketamine      object
Legalh        object
LSD           object
Meth          object
Mushrooms     object
Nicotine      object
Semer         object
VSA           object
dtype: object
In [ ]:
#We check the number of missing values
df.isnull().sum()
Out[ ]:
Age          0
Gender       0
Education    0
Ethnicity    0
Country      0
Nscore       0
Escore       0
Oscore       0
Ascore       0
Cscore       0
Impulsive    0
SS           0
Alcohol      0
Amphet       0
Amyl         0
Benzos       0
Caff         0
Cannabis     0
Choc         0
Coke         0
Crack        0
Ecstasy      0
Heroin       0
Ketamine     0
Legalh       0
LSD          0
Meth         0
Mushrooms    0
Nicotine     0
Semer        0
VSA          0
dtype: int64
In [ ]:
#We drop lines when people answered anything else than "CLO" on Semer because it is a over-claimer check
df = df.drop(df[df['Semer'] != 'CL0'].index)
In [ ]:
#We drop Ethinicity and and Semer columns due ethical reasons and beacause Semer is a fake drug
df = df.drop(['Ethnicity', 'Semer'], axis=1)
#Delete 'Ethnicty' and 'Semer' in the cols list
cols.remove('Semer')
cols.remove('Ethnicity')
cols
Out[ ]:
['ID',
 'Age',
 'Gender',
 'Education',
 'Country',
 'Nscore',
 'Escore',
 'Oscore',
 'Ascore',
 'Cscore',
 'Impulsive',
 'SS',
 'Alcohol',
 'Amphet',
 'Amyl',
 'Benzos',
 'Caff',
 'Cannabis',
 'Choc',
 'Coke',
 'Crack',
 'Ecstasy',
 'Heroin',
 'Ketamine',
 'Legalh',
 'LSD',
 'Meth',
 'Mushrooms',
 'Nicotine',
 'VSA']
In [ ]:
#On drug names columns, we consider that CL0 is 'Never Used' and CL1, CL2, CL3 as former user and CL4, CL5, CL6 as current user
df['Alcohol'] = df['Alcohol'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Amphet'] = df['Amphet'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Amyl'] = df['Amyl'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Benzos'] = df['Benzos'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Caff'] = df['Caff'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Cannabis'] = df['Cannabis'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Choc'] = df['Choc'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Coke'] = df['Coke'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Crack'] = df['Crack'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Ecstasy'] = df['Ecstasy'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Heroin'] = df['Heroin'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Ketamine'] = df['Ketamine'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Legalh'] = df['Legalh'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['LSD'] = df['LSD'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Meth'] = df['Meth'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Mushrooms'] = df['Mushrooms'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['Nicotine'] = df['Nicotine'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
df['VSA'] = df['VSA'].replace(['CL0', 'CL1', 'CL2', 'CL3', 'CL4', 'CL5', 'CL6'], ['Never Used', 'Former User', 'Former User', 'Former User', 'Current User', 'Current User', 'Current User'])
In [ ]:
#We ccreate a another dataset df_analyse which a copy of df
df_analyse = df.copy()
df_analyse.head(5)
Out[ ]:
Age Gender Education Country Nscore Escore Oscore Ascore Cscore Impulsive SS Alcohol Amphet Amyl Benzos Caff Cannabis Choc Coke Crack Ecstasy Heroin Ketamine Legalh LSD Meth Mushrooms Nicotine VSA
ID
2 -0.07854 -0.48246 1.98437 -0.31685 -0.67825 1.93886 1.43533 0.76096 -0.14277 -0.71126 -0.21575 Current User Former User Former User Never Used Current User Current User Current User Former User Never Used Current User Never Used Former User Never Used Former User Former User Never Used Current User Never Used
3 0.49788 -0.48246 -0.05921 -0.31685 -0.46725 0.80523 -0.84732 -1.62090 -1.01450 -1.37983 0.40148 Current User Never Used Never Used Never Used Current User Former User Current User Never Used Never Used Never Used Never Used Never Used Never Used Never Used Never Used Former User Never Used Never Used
4 -0.95197 0.48246 1.16365 -0.31685 -0.14882 -0.80615 -0.01928 0.59042 0.58489 -1.37983 -1.18084 Current User Never Used Never Used Former User Current User Former User Current User Former User Never Used Never Used Never Used Former User Never Used Never Used Never Used Never Used Former User Never Used
5 0.49788 0.48246 1.98437 -0.31685 0.73545 -1.63340 -0.45174 -0.30172 1.30612 -0.21712 -0.21575 Current User Former User Former User Never Used Current User Former User Current User Never Used Never Used Former User Never Used Never Used Former User Never Used Never Used Former User Former User Never Used
6 2.59171 0.48246 -1.22751 -0.31685 -0.67825 -0.30033 -1.55521 2.03972 1.63088 -1.37983 -1.54858 Former User Never Used Never Used Never Used Current User Never Used Current User Never Used Never Used Never Used Never Used Never Used Never Used Never Used Never Used Never Used Current User Never Used
In [ ]:
#We go for the categoricalize variables
from sklearn.preprocessing import OrdinalEncoder
encoder = OrdinalEncoder()
tab_enc = encoder.fit_transform(df.iloc[:,11:])
tab_enc
Out[ ]:
array([[0., 1., 1., ..., 2., 0., 2.],
       [0., 2., 2., ..., 1., 2., 2.],
       [0., 2., 2., ..., 2., 1., 2.],
       ...,
       [0., 0., 0., ..., 1., 0., 2.],
       [0., 2., 2., ..., 1., 0., 2.],
       [0., 1., 2., ..., 1., 0., 1.]])
In [ ]:
df_ord = pd.DataFrame(tab_enc, columns = cols[12:])
df_ord['ID'] = df.index
df_ord.set_index("ID", inplace=True)
df_ord
Out[ ]:
Alcohol Amphet Amyl Benzos Caff Cannabis Choc Coke Crack Ecstasy Heroin Ketamine Legalh LSD Meth Mushrooms Nicotine VSA
ID
2 0.0 1.0 1.0 2.0 0.0 0.0 0.0 1.0 2.0 0.0 2.0 1.0 2.0 1.0 1.0 2.0 0.0 2.0
3 0.0 2.0 2.0 2.0 0.0 1.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 2.0 2.0
4 0.0 2.0 2.0 1.0 0.0 1.0 0.0 1.0 2.0 2.0 2.0 1.0 2.0 2.0 2.0 2.0 1.0 2.0
5 0.0 1.0 1.0 2.0 0.0 1.0 0.0 2.0 2.0 1.0 2.0 2.0 1.0 2.0 2.0 1.0 1.0 2.0
6 1.0 2.0 2.0 2.0 0.0 2.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.0 2.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1884 0.0 2.0 2.0 2.0 0.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 2.0 2.0 2.0 0.0
1885 0.0 2.0 2.0 2.0 0.0 1.0 0.0 2.0 2.0 1.0 2.0 2.0 1.0 0.0 0.0 0.0 0.0 2.0
1886 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 1.0 2.0 1.0 2.0 1.0 0.0 2.0
1887 0.0 2.0 2.0 2.0 0.0 0.0 0.0 2.0 2.0 1.0 2.0 2.0 1.0 1.0 2.0 1.0 0.0 2.0
1888 0.0 1.0 2.0 1.0 0.0 1.0 0.0 1.0 2.0 1.0 2.0 2.0 1.0 1.0 2.0 1.0 0.0 1.0

1876 rows × 18 columns

In [ ]:
df.iloc[:,11:] = df_ord
df.head(5)
Out[ ]:
Age Gender Education Country Nscore Escore Oscore Ascore Cscore Impulsive SS Alcohol Amphet Amyl Benzos Caff Cannabis Choc Coke Crack Ecstasy Heroin Ketamine Legalh LSD Meth Mushrooms Nicotine VSA
ID
2 -0.07854 -0.48246 1.98437 -0.31685 -0.67825 1.93886 1.43533 0.76096 -0.14277 -0.71126 -0.21575 0.0 1.0 1.0 2.0 0.0 0.0 0.0 1.0 2.0 0.0 2.0 1.0 2.0 1.0 1.0 2.0 0.0 2.0
3 0.49788 -0.48246 -0.05921 -0.31685 -0.46725 0.80523 -0.84732 -1.62090 -1.01450 -1.37983 0.40148 0.0 2.0 2.0 2.0 0.0 1.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 2.0 2.0
4 -0.95197 0.48246 1.16365 -0.31685 -0.14882 -0.80615 -0.01928 0.59042 0.58489 -1.37983 -1.18084 0.0 2.0 2.0 1.0 0.0 1.0 0.0 1.0 2.0 2.0 2.0 1.0 2.0 2.0 2.0 2.0 1.0 2.0
5 0.49788 0.48246 1.98437 -0.31685 0.73545 -1.63340 -0.45174 -0.30172 1.30612 -0.21712 -0.21575 0.0 1.0 1.0 2.0 0.0 1.0 0.0 2.0 2.0 1.0 2.0 2.0 1.0 2.0 2.0 1.0 1.0 2.0
6 2.59171 0.48246 -1.22751 -0.31685 -0.67825 -0.30033 -1.55521 2.03972 1.63088 -1.37983 -1.54858 1.0 2.0 2.0 2.0 0.0 2.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.0 2.0

We go for analyse de dataset

In [ ]:
#In df, extract the unique values of the 5 first columnsin vectors
age = df_analyse['Age'].unique()
gender = df_analyse['Gender'].unique()
education = df_analyse['Education'].unique()
country = df_analyse['Country'].unique()
#Sort each vector
age.sort()
gender.sort()
education.sort()
country.sort()
#Show the vectors
print(gender)

[-0.48246  0.48246]
In [ ]:
#Replace values in Age by ages intervals
df_analyse['Age'] = df_analyse['Age'].replace(age, ['18-24', '25-34', '35-44', '45-54', '55-64', '65+'],)
#In column Gender replace values in gender by 'Male' or 'Female'
df_analyse['Gender'] = df_analyse['Gender'].replace(gender, ['Male', 'Female'])
#Remplace values in Education by 'Left school before 16 years', 'Left school at 16 years', 'Left school at 17 years', 'Left school at 18 years', 'Some college or university, no certificate or degree', 'Professional certificate/ diploma', 'University degree', 'Masters degree', 'Doctorate degree'
df_analyse['Education'] = df_analyse['Education'].replace(education, ['Left school before 16 years', 'Left school at 16 years', 'Left school at 17 years', 'Left school at 18 years', 'Some college or university, no certificate or degree', 'Professional certificate/ diploma', 'University degree', 'Masters degree', 'Doctorate degree'])
#Remplace values in Country by 'Australia', 'Canada', 'New Zealand', 'Other', 'Republic of Ireland', 'UK', 'USA'
df_analyse['Country'] = df_analyse['Country'].replace(country, ['USA', 'New Zeland', 'Other', 'Australia', 'Ireland', 'Canada', 'UK'])
#Remplace values in ethnicity by 'Asian', 'Black', 'Mixed-Black/Asian', 'Mixed-White/Asian', 'Mixed-White/Black', 'Other', 'White'
df_analyse.head()
Out[ ]:
Age Gender Education Country Nscore Escore Oscore Ascore Cscore Impulsive SS Alcohol Amphet Amyl Benzos Caff Cannabis Choc Coke Crack Ecstasy Heroin Ketamine Legalh LSD Meth Mushrooms Nicotine VSA
ID
2 25-34 Male Doctorate degree Other -0.67825 1.93886 1.43533 0.76096 -0.14277 -0.71126 -0.21575 Current User Former User Former User Never Used Current User Current User Current User Former User Never Used Current User Never Used Former User Never Used Former User Former User Never Used Current User Never Used
3 35-44 Male Professional certificate/ diploma Other -0.46725 0.80523 -0.84732 -1.62090 -1.01450 -1.37983 0.40148 Current User Never Used Never Used Never Used Current User Former User Current User Never Used Never Used Never Used Never Used Never Used Never Used Never Used Never Used Former User Never Used Never Used
4 18-24 Female Masters degree Other -0.14882 -0.80615 -0.01928 0.59042 0.58489 -1.37983 -1.18084 Current User Never Used Never Used Former User Current User Former User Current User Former User Never Used Never Used Never Used Former User Never Used Never Used Never Used Never Used Former User Never Used
5 35-44 Female Doctorate degree Other 0.73545 -1.63340 -0.45174 -0.30172 1.30612 -0.21712 -0.21575 Current User Former User Former User Never Used Current User Former User Current User Never Used Never Used Former User Never Used Never Used Former User Never Used Never Used Former User Former User Never Used
6 65+ Female Left school at 18 years Other -0.67825 -0.30033 -1.55521 2.03972 1.63088 -1.37983 -1.54858 Former User Never Used Never Used Never Used Current User Never Used Current User Never Used Never Used Never Used Never Used Never Used Never Used Never Used Never Used Never Used Current User Never Used
In [ ]:
#On graphs, we show the distribution of the columns Age Gender
fig, ax = plt.subplots(1, 2, figsize=(15, 5))
#We create a pie chart for the column Age
sns.countplot(x='Age', data=df_analyse, ax=ax[0])
#We create a pie chart for the column
sns.countplot(x='Gender', data=df_analyse, ax=ax[1])
Out[ ]:
<AxesSubplot:xlabel='Gender', ylabel='count'>
In [ ]:
#On another graph, we show the distribution of the columns Education Country
fig, ax = plt.subplots(1, 2, figsize=(50, 10))
sns.countplot(x='Education', data=df_analyse, ax=ax[0])
sns.countplot(x='Country', data=df_analyse, ax=ax[1])
Out[ ]:
<AxesSubplot:xlabel='Country', ylabel='count'>
In [ ]:
#It seems the column Country is not very useful, we drop it on df and df_analyse
df = df.drop(['Country'], axis=1)
df_analyse = df_analyse.drop(['Country'], axis=1)
In [ ]:
#We want to show on the on three plot the distribution of Coke, Cannabis and Heroin
fig, ax = plt.subplots(2, 2, figsize=(15, 10))
sns.countplot(x='Coke', data=df_analyse, ax=ax[0,0])
sns.countplot(x='Cannabis', data=df_analyse, ax=ax[0,1])
sns.countplot(x='Heroin', data=df_analyse, ax=ax[1,0])
sns.countplot(x='Alcohol', data=df_analyse, ax=ax[1,1])
Out[ ]:
<AxesSubplot:xlabel='Alcohol', ylabel='count'>
In [ ]:
#In a new columns we count the number of drug used by each person
df_analyse['Nb_drug'] = df_analyse['Coke'] + df_analyse['Cannabis'] + df_analyse['Heroin'] + df_analyse['Alcohol'] + df_analyse['Amphet'] + df_analyse['Amyl'] + df_analyse['Benzos'] + df_analyse['Caff'] + df_analyse['Choc'] + df_analyse['Coke'] + df_analyse['Crack'] + df_analyse['Ecstasy'] + df_analyse['Ketamine'] + df_analyse['Legalh'] + df_analyse['LSD'] + df_analyse['Meth'] + df_analyse['Mushrooms'] + df_analyse['Nicotine'] + df_analyse['VSA']
df_analyse['Nb_drug'] = df_analyse['Nb_drug'].str.count('Current User')
df_analyse.head(5)
Out[ ]:
Age Gender Education Nscore Escore Oscore Ascore Cscore Impulsive SS Alcohol Amphet Amyl Benzos Caff Cannabis Choc Coke Crack Ecstasy Heroin Ketamine Legalh LSD Meth Mushrooms Nicotine VSA Nb_drug
ID
2 25-34 Male Doctorate degree -0.67825 1.93886 1.43533 0.76096 -0.14277 -0.71126 -0.21575 Current User Former User Former User Never Used Current User Current User Current User Former User Never Used Current User Never Used Former User Never Used Former User Former User Never Used Current User Never Used 6
3 35-44 Male Professional certificate/ diploma -0.46725 0.80523 -0.84732 -1.62090 -1.01450 -1.37983 0.40148 Current User Never Used Never Used Never Used Current User Former User Current User Never Used Never Used Never Used Never Used Never Used Never Used Never Used Never Used Former User Never Used Never Used 3
4 18-24 Female Masters degree -0.14882 -0.80615 -0.01928 0.59042 0.58489 -1.37983 -1.18084 Current User Never Used Never Used Former User Current User Former User Current User Former User Never Used Never Used Never Used Former User Never Used Never Used Never Used Never Used Former User Never Used 3
5 35-44 Female Doctorate degree 0.73545 -1.63340 -0.45174 -0.30172 1.30612 -0.21712 -0.21575 Current User Former User Former User Never Used Current User Former User Current User Never Used Never Used Former User Never Used Never Used Former User Never Used Never Used Former User Former User Never Used 3
6 65+ Female Left school at 18 years -0.67825 -0.30033 -1.55521 2.03972 1.63088 -1.37983 -1.54858 Former User Never Used Never Used Never Used Current User Never Used Current User Never Used Never Used Never Used Never Used Never Used Never Used Never Used Never Used Never Used Current User Never Used 3
In [ ]:
#Show on a graph the means of drug used by users curently using Cannabis, Alcohol, Heroin, Cocaine
fig, ax = plt.subplots(1, 4, figsize=(15, 5))
sns.barplot(x='Cannabis', y='Nb_drug', data=df_analyse, ax=ax[0])
sns.barplot(x='Alcohol', y='Nb_drug', data=df_analyse, ax=ax[1])
sns.barplot(x='Heroin', y='Nb_drug', data=df_analyse, ax=ax[2])
sns.barplot(x='Coke', y='Nb_drug', data=df_analyse, ax=ax[3])
Out[ ]:
<AxesSubplot:xlabel='Coke', ylabel='Nb_drug'>
In [ ]:
#In df, Nscore Escore Oscore Ascore Cscore are the 5 personality traits
import plotly.graph_objects as go
from plotly.subplots import make_subplots

#Make a radar chart of the mean of each personality trait
fig = go.Figure()
fig.add_trace(go.Scatterpolar(
        r=[df_analyse['Nscore'].mean(), df_analyse['Escore'].mean(), df_analyse['Oscore'].mean(), df_analyse['Ascore'].mean(), df_analyse['Cscore'].mean()],
        theta=['Nscore', 'Escore', 'Oscore', 'Ascore', 'Cscore'],
        fill='toself',
        name='Average'
))
fig.update_layout(
        polar=dict(
        radialaxis=dict(
        visible=True,
        range=[-3.5, 3.5]
        )),
        showlegend=True
)

#Add to this same plot the mean of each personality trait for cannabis users
fig.add_trace(go.Scatterpolar(
        r=[df_analyse[df_analyse['Cannabis'] == 'Current User']['Nscore'].mean(), df_analyse[df_analyse['Cannabis'] == 'Current User']['Escore'].mean(), df_analyse[df_analyse['Cannabis'] == 'Current User']['Oscore'].mean(), df_analyse[df_analyse['Cannabis'] == 'Current User']['Ascore'].mean(), df_analyse[df_analyse['Cannabis'] == 'Current User']['Cscore'].mean()],
        theta=['Nscore', 'Escore', 'Oscore', 'Ascore', 'Cscore'],
        fill='toself',
        name='Cannabis'
))
fig.update_layout(
        polar=dict(
        radialaxis=dict(
        visible=True,
        range=[-3.5, 3.5]
        )),
        showlegend=True
)
#Add to this same plot the mean of each personality trait for Crack users
fig.add_trace(go.Scatterpolar(
        r=[df_analyse[df_analyse['Crack'] == 'Current User']['Nscore'].mean(), df_analyse[df_analyse['Crack'] == 'Current User']['Escore'].mean(), df_analyse[df_analyse['Crack'] == 'Current User']['Oscore'].mean(), df_analyse[df_analyse['Crack'] == 'Current User']['Ascore'].mean(), df_analyse[df_analyse['Crack'] == 'Current User']['Cscore'].mean()],
        theta=['Nscore', 'Escore', 'Oscore', 'Ascore', 'Cscore'],
        fill='toself',
        name='Crack'
))
fig.update_layout(
        polar=dict(
        radialaxis=dict(
        visible=True,
        range=[-3.5, 3.5]
        )),
        showlegend=True
)
fig.update_layout(
        autosize=False,
        width=1000,
        height=1000,
        margin=dict(
        l=50,
        r=50,
        b=100,
        t=100,
        pad=4
        ),
        paper_bgcolor="LightSteelBlue",
)


#Add to this same plot the mean of each personality trait for Ecstasy users
fig.add_trace(go.Scatterpolar(
        r=[df_analyse[df_analyse['Ecstasy'] == 'Current User']['Nscore'].mean(), df_analyse[df_analyse['Ecstasy'] == 'Current User']['Escore'].mean(), df_analyse[df_analyse['Ecstasy'] == 'Current User']['Oscore'].mean(), df_analyse[df_analyse['Ecstasy'] == 'Current User']['Ascore'].mean(), df_analyse[df_analyse['Ecstasy'] == 'Current User']['Cscore'].mean()],
        theta=['Nscore', 'Escore', 'Oscore', 'Ascore', 'Cscore'],
        fill='toself',
        name='Ecstasy'
))
fig.update_layout(
        polar=dict(
        radialaxis=dict(
        visible=True,
        range=[-3.5, 3.5]
        )),
        showlegend=True
)
fig.show(renderer='notebook')
In [ ]:
#On another radar chart show the mean of each personality trait for any drug users and for any non drug users
fig = go.Figure()
fig.add_trace(go.Scatterpolar(
        r=[df_analyse[df_analyse['Nb_drug'] > 0]['Nscore'].mean(), df_analyse[df_analyse['Nb_drug'] > 0]['Escore'].mean(), df_analyse[df_analyse['Nb_drug'] > 0]['Oscore'].mean(), df_analyse[df_analyse['Nb_drug'] > 0]['Ascore'].mean(), df_analyse[df_analyse['Nb_drug'] > 0]['Cscore'].mean()],
        theta=['Nscore', 'Escore', 'Oscore', 'Ascore', 'Cscore'],
        fill='toself',
        name='Drug users'
))
fig.update_layout(
        polar=dict(
        radialaxis=dict(
        visible=True,
        range=[-3.5, 3.5]
        )),
        showlegend=True
)
fig.add_trace(go.Scatterpolar(
        r=[df_analyse[df_analyse['Nb_drug'] == 0]['Nscore'].mean(), df_analyse[df_analyse['Nb_drug'] == 0]['Escore'].mean(), df_analyse[df_analyse['Nb_drug'] == 0]['Oscore'].mean(), df_analyse[df_analyse['Nb_drug'] == 0]['Ascore'].mean(), df_analyse[df_analyse['Nb_drug'] == 0]['Cscore'].mean()],
        theta=['Nscore', 'Escore', 'Oscore', 'Ascore', 'Cscore'],
        fill='toself',
        name='Non drug users'
))
fig.update_layout(
        polar=dict(
        radialaxis=dict(
        visible=True,
        range=[-3.5, 3.5]
        )),
        showlegend=True
)
fig.update_layout(
        autosize=False,
        width=1000,
        height=1000,
        margin=dict(
        l=50,
        r=50,
        b=100,
        t=100,
        pad=4
        ),
        paper_bgcolor="LightSteelBlue",
)
fig.show(renderer='notebook')
In [ ]:
#We show the correlation matrix with df
corr = df.corr()
plt.figure(figsize=(20,10))
sns.heatmap(corr, annot=True, vmin=-1)
Out[ ]:
<AxesSubplot:>
In [ ]:
#Show the boxplot of each feature of df on a plpt
fig = plt.figure(figsize=(20,10))
ax = fig.add_subplot(111)
bp = ax.boxplot(df.values)
ax.set_xticklabels(df.columns)
plt.show()
#We observe that the features are not normally distributed, it will be needed to normalize them
In [ ]:
#We create a sub dataframe that is a copy of df
df_Cannabis = df.copy()
#In a new column named Cannabis use, we put 1 if the person has used Cannabis and 0 if not, that means that the value of the column Cannabis is 2
df_Cannabis['Cannabis use'] = df_Cannabis['Cannabis'].apply(lambda x: 1 if x == 2 else 0)
df_Cannabis = df_Cannabis.drop(['Cannabis'], axis=1)
df_Cannabis.head(90)
Out[ ]:
Age Gender Education Nscore Escore Oscore Ascore Cscore Impulsive SS Alcohol Amphet Amyl Benzos Caff Choc Coke Crack Ecstasy Heroin Ketamine Legalh LSD Meth Mushrooms Nicotine VSA Cannabis use
ID
2 -0.07854 -0.48246 1.98437 -0.67825 1.93886 1.43533 0.76096 -0.14277 -0.71126 -0.21575 0.0 1.0 1.0 2.0 0.0 0.0 1.0 2.0 0.0 2.0 1.0 2.0 1.0 1.0 2.0 0.0 2.0 0
3 0.49788 -0.48246 -0.05921 -0.46725 0.80523 -0.84732 -1.62090 -1.01450 -1.37983 0.40148 0.0 2.0 2.0 2.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 2.0 2.0 0
4 -0.95197 0.48246 1.16365 -0.14882 -0.80615 -0.01928 0.59042 0.58489 -1.37983 -1.18084 0.0 2.0 2.0 1.0 0.0 0.0 1.0 2.0 2.0 2.0 1.0 2.0 2.0 2.0 2.0 1.0 2.0 0
5 0.49788 0.48246 1.98437 0.73545 -1.63340 -0.45174 -0.30172 1.30612 -0.21712 -0.21575 0.0 1.0 1.0 2.0 0.0 0.0 2.0 2.0 1.0 2.0 2.0 1.0 2.0 2.0 1.0 1.0 2.0 0
6 2.59171 0.48246 -1.22751 -0.67825 -0.30033 -1.55521 2.03972 1.63088 -1.37983 -1.54858 1.0 2.0 2.0 2.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.0 2.0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
87 0.49788 -0.48246 -0.61113 0.73545 -0.15487 -1.11902 0.13136 -0.14277 -0.21712 0.40148 0.0 2.0 1.0 2.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.0 2.0 0
88 1.82213 -0.48246 -1.73790 -0.92104 0.16767 -0.45174 -0.30172 0.25953 -0.71126 0.40148 0.0 2.0 2.0 2.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0
89 0.49788 0.48246 -0.61113 0.31287 -1.92173 -1.42424 -0.76096 -0.52745 -1.37983 -2.07848 1.0 2.0 1.0 1.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 2.0 0
90 0.49788 0.48246 1.16365 0.91093 -0.57545 0.29338 -0.60633 -0.27607 0.52975 -0.21575 0.0 1.0 1.0 1.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 2.0 1.0 2.0 1.0 1
91 0.49788 0.48246 1.16365 1.37297 0.32197 0.72330 0.28783 -0.78155 -1.37983 0.07987 0.0 2.0 2.0 2.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.0 2.0 0

90 rows × 28 columns

In [ ]:
#We will now try to predict if a person has used Cannabis or not
#We using the the df_Cannabis dataframe as training data

from sklearn.model_selection import train_test_split
#We split the data into training and testing data
X_train, X_test, y_train, y_test = train_test_split(df_Cannabis.drop(['Cannabis use'], axis=1), df_Cannabis['Cannabis use'], test_size=0.2, random_state=42)

#Then we wille standardize the data of the training and testing data 
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

#We show the boxplot of each feature of X_train on a plot
fig = plt.figure(figsize=(20,10))
ax = fig.add_subplot(111)
bp = ax.boxplot(X_train)
ax.set_xticklabels(df_Cannabis.drop(['Cannabis use'], axis=1).columns)
plt.show()
In [ ]:
#We will try to predict the Cannabis use with 5 different models
#We will use Random Forest, Logistic Regression, Support Vector Machine, Decision Tree and k-Nearest Neighbors

from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import classification_report, confusion_matrix

df_results = pd.DataFrame(columns=['Model', 'Accuracy', 'F1-score', 'Recall'])
model = [RandomForestClassifier(), LogisticRegression(), SVC(), DecisionTreeClassifier()]

#We will use a for loop to train the models and to show the classification report
#We will use a dataframe to store the accuracy, f1-score and recall of each model
for model in model:
    #We do this 10 times to have a better idea of the accuracy of the model
    for i in range(10):
        model.fit(X_train, y_train)
        predictions = model.predict(X_test)
        df_results = df_results.append({'Model': model, 'Accuracy': classification_report(y_test, predictions, output_dict=True)['accuracy'], 'F1-score': classification_report(y_test, predictions, output_dict=True)['1']['f1-score'], 'Recall': classification_report(y_test, predictions, output_dict=True)['1']['recall']}, ignore_index=True)
        
#We print df_results and group by the model to have the mean of the accuracy, f1-score and recall of each model
df_results['Model'] = df_results['Model'].apply(lambda x: x.__class__.__name__)
df_results
Out[ ]:
Model Accuracy F1-score Recall
0 RandomForestClassifier 0.872340 0.727273 0.680851
1 RandomForestClassifier 0.882979 0.752809 0.712766
2 RandomForestClassifier 0.875000 0.737430 0.702128
3 RandomForestClassifier 0.882979 0.755556 0.723404
4 RandomForestClassifier 0.882979 0.752809 0.712766
5 RandomForestClassifier 0.882979 0.747126 0.691489
6 RandomForestClassifier 0.890957 0.773481 0.744681
7 RandomForestClassifier 0.885638 0.754286 0.702128
8 RandomForestClassifier 0.869681 0.723164 0.680851
9 RandomForestClassifier 0.877660 0.744444 0.712766
10 LogisticRegression 0.880319 0.773869 0.819149
11 LogisticRegression 0.880319 0.773869 0.819149
12 LogisticRegression 0.880319 0.773869 0.819149
13 LogisticRegression 0.880319 0.773869 0.819149
14 LogisticRegression 0.880319 0.773869 0.819149
15 LogisticRegression 0.880319 0.773869 0.819149
16 LogisticRegression 0.880319 0.773869 0.819149
17 LogisticRegression 0.880319 0.773869 0.819149
18 LogisticRegression 0.880319 0.773869 0.819149
19 LogisticRegression 0.880319 0.773869 0.819149
20 SVC 0.864362 0.738462 0.765957
21 SVC 0.864362 0.738462 0.765957
22 SVC 0.864362 0.738462 0.765957
23 SVC 0.864362 0.738462 0.765957
24 SVC 0.864362 0.738462 0.765957
25 SVC 0.864362 0.738462 0.765957
26 SVC 0.864362 0.738462 0.765957
27 SVC 0.864362 0.738462 0.765957
28 SVC 0.864362 0.738462 0.765957
29 SVC 0.864362 0.738462 0.765957
30 DecisionTreeClassifier 0.811170 0.643216 0.680851
31 DecisionTreeClassifier 0.821809 0.666667 0.712766
32 DecisionTreeClassifier 0.824468 0.666667 0.702128
33 DecisionTreeClassifier 0.827128 0.670051 0.702128
34 DecisionTreeClassifier 0.819149 0.660000 0.702128
35 DecisionTreeClassifier 0.805851 0.633166 0.670213
36 DecisionTreeClassifier 0.821809 0.663317 0.702128
37 DecisionTreeClassifier 0.813830 0.646465 0.680851
38 DecisionTreeClassifier 0.819149 0.660000 0.702128
39 DecisionTreeClassifier 0.832447 0.673575 0.691489
In [ ]:
#We show the boxplot of the accuracy, f1-score and recall of each model
fig = plt.figure(figsize=(20,10))
ax = fig.add_subplot(111)
bp = ax.boxplot(df_results[['Accuracy', 'F1-score', 'Recall']].values)
ax.set_xticklabels(df_results[['Accuracy', 'F1-score', 'Recall']].columns)
plt.show()
In [ ]:
#We group by the model to have the mean of the accuracy, f1-score and recall of each model
df_results = df_results.groupby('Model').mean()
#We show on a curve graph the mean of the accuracy, f1-score and recall of each model
df_results.plot(kind='bar', figsize=(20,10))
Out[ ]:
<AxesSubplot:xlabel='Model'>
In [ ]:
#We gonna use grid search to find the best parameters for the Random Forest model and Logistic Regression model
from sklearn.model_selection import GridSearchCV

#We create a list of parameters for the Random Forest model with 4 different values for each parameters
param_grid = {'n_estimators': [10, 50, 100, 200], 'max_depth': [10, 50, 100, 200], 'min_samples_split': [2, 5, 10, 20], 'min_samples_leaf': [1, 2, 4, 8]}
grid = GridSearchCV(RandomForestClassifier(), param_grid, refit=True, verbose=2)
grid.fit(X_train, y_train)
print("The best parameters are: ", grid.best_params_)
#We print the best parameters with a dataframe
pd.DataFrame(grid.cv_results_).sort_values(by='rank_test_score').head(1)

Fitting 5 folds for each of 256 candidates, totalling 1280 fits
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.5s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.5s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.5s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.3s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   1.5s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   1.5s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   1.2s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.7s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   1.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   1.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   1.3s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   1.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.7s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.7s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=10, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   1.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   1.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.5s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   1.3s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.5s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.5s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   1.1s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   1.1s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   1.9s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.5s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.7s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.5s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.5s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   1.5s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   1.5s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   1.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   1.6s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   1.5s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   1.5s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   1.9s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   1.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.5s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   1.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   1.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.6s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.5s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   1.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   1.0s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=50, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   1.3s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   1.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   1.3s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   1.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.3s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.6s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.6s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.6s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.6s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.6s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   1.3s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   1.3s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   1.5s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   1.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   1.3s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.3s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.3s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   1.1s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.5s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.5s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   1.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.5s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   1.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.5s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   1.1s
[CV] END max_depth=100, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.3s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.5s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.5s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.5s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.5s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   1.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=100, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.1s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=   1.1s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   0.9s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   1.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   1.1s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=   1.1s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   1.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.9s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=   0.8s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   1.4s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   1.3s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=200, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=   0.8s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.4s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.7s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.7s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.7s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.7s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.7s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.7s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.7s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.7s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.7s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.7s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=10; total time=   0.0s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.1s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.2s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   0.3s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.6s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.5s
[CV] END max_depth=200, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=   0.6s
The best parameters are:  {'max_depth': 50, 'min_samples_leaf': 1, 'min_samples_split': 20, 'n_estimators': 50}
Out[ ]:
mean_fit_time std_fit_time mean_score_time std_score_time param_max_depth param_min_samples_leaf param_min_samples_split param_n_estimators params split0_test_score split1_test_score split2_test_score split3_test_score split4_test_score mean_test_score std_test_score rank_test_score
77 0.205303 0.005386 0.020715 0.00373 50 1 20 50 {'max_depth': 50, 'min_samples_leaf': 1, 'min_... 0.91 0.883333 0.906667 0.91 0.91 0.904 0.010414 1
In [ ]:
#We store the best parameters in a dictionary name best_params_RandomForest
best_params_RandomForest = grid.best_params_
In [ ]:
#We use research grid search to find the best parameters for the Logistic Regression model
#We create a list of parameters for the Logistic Regression model with 4 different values for each parameters
param_grid = {'C': [0.1, 1, 10, 100], 'penalty': ['l1', 'l2'], 'solver': ['liblinear', 'saga']}
grid = GridSearchCV(LogisticRegression(), param_grid, refit=True, verbose=2)
grid.fit(X_train, y_train)
#We print the best parameters with sentenses
print("The best parameters are: ", grid.best_params_)
#We print the best parameters with a dataframe
pd.DataFrame(grid.cv_results_).sort_values(by='rank_test_score').head(1)

Fitting 5 folds for each of 16 candidates, totalling 80 fits
[CV] END ................C=0.1, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END ................C=0.1, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END ................C=0.1, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END ................C=0.1, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END ................C=0.1, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END .....................C=0.1, penalty=l1, solver=saga; total time=   0.0s
[CV] END .....................C=0.1, penalty=l1, solver=saga; total time=   0.0s
[CV] END .....................C=0.1, penalty=l1, solver=saga; total time=   0.0s
[CV] END .....................C=0.1, penalty=l1, solver=saga; total time=   0.0s
[CV] END .....................C=0.1, penalty=l1, solver=saga; total time=   0.0s
[CV] END ................C=0.1, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ................C=0.1, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ................C=0.1, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ................C=0.1, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ................C=0.1, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END .....................C=0.1, penalty=l2, solver=saga; total time=   0.0s
[CV] END .....................C=0.1, penalty=l2, solver=saga; total time=   0.0s
[CV] END .....................C=0.1, penalty=l2, solver=saga; total time=   0.0s
[CV] END .....................C=0.1, penalty=l2, solver=saga; total time=   0.0s
[CV] END .....................C=0.1, penalty=l2, solver=saga; total time=   0.0s
[CV] END ..................C=1, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END ..................C=1, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END ..................C=1, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END ..................C=1, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END ..................C=1, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END .......................C=1, penalty=l1, solver=saga; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END .......................C=1, penalty=l1, solver=saga; total time=   0.0s
[CV] END .......................C=1, penalty=l1, solver=saga; total time=   0.1s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END .......................C=1, penalty=l1, solver=saga; total time=   0.0s
[CV] END .......................C=1, penalty=l1, solver=saga; total time=   0.0s
[CV] END ..................C=1, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ..................C=1, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ..................C=1, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ..................C=1, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ..................C=1, penalty=l2, solver=liblinear; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END .......................C=1, penalty=l2, solver=saga; total time=   0.0s
[CV] END .......................C=1, penalty=l2, solver=saga; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END .......................C=1, penalty=l2, solver=saga; total time=   0.0s
[CV] END .......................C=1, penalty=l2, solver=saga; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END .......................C=1, penalty=l2, solver=saga; total time=   0.0s
[CV] END .................C=10, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END .................C=10, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END .................C=10, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END .................C=10, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END .................C=10, penalty=l1, solver=liblinear; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END ......................C=10, penalty=l1, solver=saga; total time=   0.1s
[CV] END ......................C=10, penalty=l1, solver=saga; total time=   0.1s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END ......................C=10, penalty=l1, solver=saga; total time=   0.1s
[CV] END ......................C=10, penalty=l1, solver=saga; total time=   0.1s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END ......................C=10, penalty=l1, solver=saga; total time=   0.0s
[CV] END .................C=10, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END .................C=10, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END .................C=10, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END .................C=10, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END .................C=10, penalty=l2, solver=liblinear; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END ......................C=10, penalty=l2, solver=saga; total time=   0.1s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END ......................C=10, penalty=l2, solver=saga; total time=   0.2s
[CV] END ......................C=10, penalty=l2, solver=saga; total time=   0.0s
[CV] END ......................C=10, penalty=l2, solver=saga; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END ......................C=10, penalty=l2, solver=saga; total time=   0.1s
[CV] END ................C=100, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END ................C=100, penalty=l1, solver=liblinear; total time=   0.0s
[CV] END ................C=100, penalty=l1, solver=liblinear; total time=   0.1s
[CV] END ................C=100, penalty=l1, solver=liblinear; total time=   0.3s
[CV] END ................C=100, penalty=l1, solver=liblinear; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END .....................C=100, penalty=l1, solver=saga; total time=   0.0s
[CV] END .....................C=100, penalty=l1, solver=saga; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END .....................C=100, penalty=l1, solver=saga; total time=   0.0s
[CV] END .....................C=100, penalty=l1, solver=saga; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END .....................C=100, penalty=l1, solver=saga; total time=   0.0s
[CV] END ................C=100, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ................C=100, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ................C=100, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ................C=100, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END ................C=100, penalty=l2, solver=liblinear; total time=   0.0s
[CV] END .....................C=100, penalty=l2, solver=saga; total time=   0.0s

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge


[CV] END .....................C=100, penalty=l2, solver=saga; total time=   0.0s
[CV] END .....................C=100, penalty=l2, solver=saga; total time=   0.0s
[CV] END .....................C=100, penalty=l2, solver=saga; total time=   0.0s
[CV] END .....................C=100, penalty=l2, solver=saga; total time=   0.0s
The best parameters are:  {'C': 0.1, 'penalty': 'l2', 'solver': 'saga'}

C:\Users\bouas\AppData\Roaming\Python\Python39\site-packages\sklearn\linear_model\_sag.py:350: ConvergenceWarning:

The max_iter was reached which means the coef_ did not converge
Out[ ]:
mean_fit_time std_fit_time mean_score_time std_score_time param_C param_penalty param_solver params split0_test_score split1_test_score split2_test_score split3_test_score split4_test_score mean_test_score std_test_score rank_test_score
3 0.040099 0.003616 0.00047 0.00058 0.1 l2 saga {'C': 0.1, 'penalty': 'l2', 'solver': 'saga'} 0.906667 0.866667 0.9 0.906667 0.88 0.892 0.016 1
In [ ]:
#We store the best parameters in another dictionary
best_params_lr = grid.best_params_
In [ ]:
#We use grid search to find the best parameters for the Support Vector Machine model
#We create a list of parameters for the Support Vector Machine model with 4 different values for each parameters
param_grid = {'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001], 'kernel': ['rbf', 'poly', 'sigmoid']}
grid = GridSearchCV(SVC(), param_grid, refit=True, verbose=2)
grid.fit(X_train, y_train)
#We print the best parameters with sentenses    
print("The best parameters are: ", grid.best_params_)
#We print the best parameters with a dataframe
pd.DataFrame(grid.cv_results_).sort_values(by='rank_test_score').head(1)

Fitting 5 folds for each of 48 candidates, totalling 240 fits
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.2s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.3s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.2s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.4s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.3s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.1s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.1s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.1s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.2s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.2s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.1s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.2s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.3s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.1s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.1s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.1s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.1s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.1s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.2s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.4s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.4s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.6s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.3s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.1s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.1s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.1s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.2s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.1s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.2s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.1s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.1s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.1s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.1s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.1s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.1s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.1s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.1s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.1s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.1s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.1s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.1s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.1s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.3s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.6s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.4s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.7s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.3s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   0.1s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   0.2s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   0.2s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   0.0s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   0.3s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.2s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.2s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.4s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.1s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.2s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.1s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.2s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.1s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.1s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.1s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.1s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.1s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.1s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.1s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.6s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.2s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.1s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.2s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.1s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.1s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.2s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.4s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.2s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.1s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
The best parameters are:  {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}
Out[ ]:
mean_fit_time std_fit_time mean_score_time std_score_time param_C param_gamma param_kernel params split0_test_score split1_test_score split2_test_score split3_test_score split4_test_score mean_test_score std_test_score rank_test_score
42 0.078602 0.014156 0.022805 0.004585 100 0.01 rbf {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'} 0.903333 0.863333 0.896667 0.913333 0.886667 0.892667 0.017049 1
In [ ]:
#We store the best parameters in another dictionary
best_params_SVM = grid.best_params_
In [ ]:
#Now we we will use the best parameters to train the Random Forest model, the Logistic Regression model and the Support Vector Machine model
#We will use a dataframe to store the accuracy, f1-score and recall of each model
df_results_2 = pd.DataFrame(columns=['Model', 'Accuracy', 'F1-score', 'Recall'])
model = [RandomForestClassifier(**best_params_RandomForest), LogisticRegression(**best_params_lr), SVC(**best_params_SVM)]
for model in model:
    for i in range(10):
        model.fit(X_train, y_train)
        predictions = model.predict(X_test)
    #We store the accuracy, f1-score and recall of each model in the dataframe
        df_results_2 = df_results_2.append({'Model': model, 'Accuracy': classification_report(y_test, predictions, output_dict=True)['accuracy'], 'F1-score': classification_report(y_test, predictions, output_dict=True)['1']['f1-score'], 'Recall': classification_report(y_test, predictions, output_dict=True)['1']['recall']}, ignore_index=True)
df_results_2['Model'] = df_results_2['Model'].apply(lambda x: x.__class__.__name__)
df_results_2
Out[ ]:
Model Accuracy F1-score Recall
0 RandomForestClassifier 0.875000 0.734463 0.691489
1 RandomForestClassifier 0.885638 0.757062 0.712766
2 RandomForestClassifier 0.882979 0.750000 0.702128
3 RandomForestClassifier 0.880319 0.748603 0.712766
4 RandomForestClassifier 0.882979 0.752809 0.712766
5 RandomForestClassifier 0.882979 0.752809 0.712766
6 RandomForestClassifier 0.882979 0.755556 0.723404
7 RandomForestClassifier 0.885638 0.751445 0.691489
8 RandomForestClassifier 0.880319 0.745763 0.702128
9 RandomForestClassifier 0.885638 0.762431 0.734043
10 LogisticRegression 0.875000 0.761421 0.797872
11 LogisticRegression 0.875000 0.761421 0.797872
12 LogisticRegression 0.875000 0.761421 0.797872
13 LogisticRegression 0.875000 0.761421 0.797872
14 LogisticRegression 0.875000 0.761421 0.797872
15 LogisticRegression 0.875000 0.761421 0.797872
16 LogisticRegression 0.875000 0.761421 0.797872
17 LogisticRegression 0.875000 0.761421 0.797872
18 LogisticRegression 0.875000 0.761421 0.797872
19 LogisticRegression 0.875000 0.761421 0.797872
20 SVC 0.869681 0.751269 0.787234
21 SVC 0.869681 0.751269 0.787234
22 SVC 0.869681 0.751269 0.787234
23 SVC 0.869681 0.751269 0.787234
24 SVC 0.869681 0.751269 0.787234
25 SVC 0.869681 0.751269 0.787234
26 SVC 0.869681 0.751269 0.787234
27 SVC 0.869681 0.751269 0.787234
28 SVC 0.869681 0.751269 0.787234
29 SVC 0.869681 0.751269 0.787234
In [ ]:
#We show the boxplot of the accuracy, f1-score and recall of each model
fig = plt.figure(figsize=(20,10))
ax = fig.add_subplot(111)
bp = ax.boxplot(df_results_2[['Accuracy', 'F1-score', 'Recall']].values)
ax.set_xticklabels(df_results_2[['Accuracy', 'F1-score', 'Recall']].columns)
plt.show()
In [ ]:
#We group by the model to have the mean of the accuracy, f1-score and recall of each model
df_results_2 = df_results_2.groupby('Model').mean()
In [ ]:
#We compare the accuracy, f1-score and recall of the models with the best parameters and the models without the best parameters
df_results_2.plot(kind='bar', figsize=(20,5))
df_results.plot(kind='bar', figsize=(20,5))
plt.xticks(rotation=0)
plt.show()
In [ ]:
#Show side by side the accuracy, f1-score and recall of the models with the best parameters and the models without the best parameters
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20,5))
df_results_2.plot(kind='bar', ax=axes[0])
df_results.plot(kind='bar', ax=axes[1])
axes[0].set_title('With best parameters')
axes[1].set_title('Without best parameters')
plt.show()
In [ ]:
#We show on a sns heatmap the confusion matrix of the Random Forest model, the Logistic Regression model and the SVC
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(25,5))
sns.heatmap(confusion_matrix(y_test, RandomForestClassifier(**best_params_RandomForest).fit(X_train, y_train).predict(X_test)), annot=True, ax=axes[0], fmt='g')
sns.heatmap(confusion_matrix(y_test, LogisticRegression(**best_params_lr).fit(X_train, y_train).predict(X_test)), annot=True, ax=axes[1], fmt='g')
sns.heatmap(confusion_matrix(y_test, SVC(**best_params_SVM).fit(X_train, y_train).predict(X_test)), annot=True, ax=axes[2], fmt='g')
axes[0].set_title('Random Forest')
axes[1].set_title('Logistic Regression')
axes[2].set_title('SVC')
plt.show()
In [ ]:
#We save the random forest model with the best parameters as 'model_cannabis.pkl'
import pickle
pickle.dump(RandomForestClassifier(**best_params_RandomForest).fit(X_train, y_train), open('model_cannabis.pkl', 'wb'))
In [ ]:
#Convert to to a dataframe X_test
X_test = pd.DataFrame(X_test)
#Take randomly 5 rows from X_test and save them as json files named input_1.json, input_2.json, input_3.json, input_4.json and input_5.json
X_test.sample(5).to_json('input_1.json', orient='records')
X_test.sample(5).to_json('input_2.json', orient='records')
X_test.sample(5).to_json('input_3.json', orient='records')
X_test.sample(5).to_json('input_4.json', orient='records')
X_test.sample(5).to_json('input_5.json', orient='records')